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IBM makes AI tools and resources available to coronavirus researchers

April 5, 2020 Big Data

IBM today announced the launch of services intended to furnish researchers with resources to fight the novel coronavirus. The company made molecules identified by AI as therapeutic candidates available under an open license, and it introduced a free version of its Functional Genomics Platform to support genome features discovery. Additionally, it provided free access to over 1,000 pieces of evidence-based curated COVID-19 and infectious disease content, and it rolled out an AI search engine trained on the COVID-19 Open Research Dataset to allow researchers to quickly find answers to questions.

According to Dario Gil, director of IBM Research, IBM created a new AI-generative framework that can rapidly create novel peptides, proteins, drug candidates, and materials, which it applied against three targets to create 3,000 new small molecules as potential COVID-19 therapeutic candidates. Researchers can study them via an interactive tool to understand their characteristics and relationship to COVID-19, and to identify molecules that might have desirable properties to be pursued in drug development.

“The traditional drug discovery pipeline relies on a library of compounds that are screened, improved, and tested to determine safety and efficacy,” said Gil. “In dealing with new pathogens such as SARS-CoV-2, there is the potential to enhance the compound libraries with additional novel compounds.”

The Functional Genomics Platform complements this with a cloud-based repository of genes, proteins, and other molecular targets from sequenced viral and bacterial organisms, with connections precomputed to help speed up the discovery of molecular targets required for drug design, test development, and treatment. Researchers working on COVID-19 can now request access to the interface; previously, it was only available to government agencies, academic institutions, and other organizations for bacterial study.

In a related development, starting today, clinicians and health care workers will gain access to COVID-19 content from IBM: Micromedex and EBSCO DynaMed, two holistic drug information and medical decision support platforms. (Micromedex is used by more than 4,500 hospitals and health systems worldwide, while DynaMed provides peer-reviewed clinical content including literature reviews in 28 specialties.) They’ll be able to take advantage of state-of-the-art drug and disease data and provide patients with educational handouts containing actionable information.

Lastly, IBM says it trained an AI search system on the COVID-19 Open Research Dataset, which contains thousands of scientific papers prepared by the White House and a coalition of research groups, in addition to licensed databases from DrugBank and GenBank. The freely available tool extracts embedded text, tables, and graphics to answer specific queries from vetted researchers.

Watson Assistant for Citizens’ debut comes after IBM made available a map on The Weather Channel to track the spread of COVID-19, mainly using data from governments as well as the World Health Organization. The company also built a dashboard on top of its Cognos Analytics suite that’s designed to help researchers, data scientists, and media analyze and filter coronavirus information down to the county level.

IBM this week debuted a chatbot that answers coronavirus questions by phone or text, and last week, the company announced it would coordinate an effort to make supercomputing capacity available to researchers in order to help identify treatments, viable mitigation strategies, and vaccines for COVID-19. IBM also launched a new Call for Code Global Challenge that will encourage developers to build open source technologies that address several areas, including crisis communication during an emergency, ways to improve remote learning, and how to inspire cooperative local communities.

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Researchers propose paradigm that trains AI agents through evolution

March 25, 2020 Big Data

A paper published by researchers at Carnegie Mellon University, San Francisco research firm OpenAI, Facebook AI Research, the University of California at Berkeley, and Shanghai Jiao Tong University describes a paradigm that scales up multi-agent reinforcement learning, where AI models learn by having agents interact within an environment such that the agent population increases in size over time. By maintaining sets of agents in each training stage and performing mix-and-match and fine-tuning steps over these sets, the coauthors say the paradigm — Evolutionary Population Curriculum — is able to promote agents with the best adaptability to the next stage.

In computer science, evolutionary computation is the family of algorithms for global optimization inspired by biological evolution. Instead of following explicit mathematical gradients, these models generate variants, test them, and retain the top performers. They’ve shown promise in early work by OpenAI, Google, Uber, and others, but they’re somewhat tough to prototype because there’s a dearth of tools targeting evolutionary algorithms and natural evolution strategies (NES).

As the coauthors explain, Evolutionary Population Curriculum allows the scaling up of agents exponentially. The core idea is to divide the learning procedure into multiple stages with an increasing number of agents in the environment, so that the agents first learn to interact in simpler scenarios with fewer agents and then leverage these experiences to adapt to more agents.

Researchers propose paradigm that trains AI agents through evolution

Above: Evolutionary Population Curriculum applied to agents ‘playing’ a Grassland Game.

Image Credit: Evolutionary Population Curriculum

Evolutionary Population Curriculum introduces new agents by directly cloning existing ones from the previous stage, but it incorporates techniques to ensure that only agents with the best adaptation abilities move onto the next stage as the population is scaled up. Crossover, mutation, and selection is performed among sets of agents in each stage in parallel so that the influence on overall training time is minimized.

The researchers experimented on three challenging environments: a predator-prey-style Grassland game, a mixed cooperative and competitive Adversarial Battle game, and a fully cooperative Food Collection game. They report that the agent “significantly” improved over baselines in terms of performance and training stability, indicating that Evolutionary Population Curriculum is general and can potentially benefit scaling other algorithms.

“Most real-world problems involve interactions between multiple agents and the problem becomes significantly harder when there exist complex cooperation and competition among agents,” wrote the coauthors. “We hope that learning with a large population of agents can also lead to the emergence of swarm intelligence in environments with simple rules in the future.”

If indeed Evolutionary Population Curriculum is an effective way of isolating the best algorithms for various target tasks, it could help to automate the most laborious bits of AI model engineering. According to an Algorithmia study, 50% of companies spend between 8 and 90 days deploying a single AI model.

The code is available in open source on GitHub.

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Google researchers use multiple cameras to reduce error rates for robot insertion and stacking tasks

February 25, 2020 Big Data

Object-manipulating robots rely on cameras to make sense of the world around them, but these cameras often require careful installation and ongoing calibration and maintenance. A new study published by researchers at Google’s Robotics division and Columbia University proposes a solution, which involves a technique that learns to accomplish tasks using multiple color cameras without an explicit 3D representation. They say that it achieves superior task performance on difficult stacking and insertion tasks compared with baselines.

This latest work builds on Google’s vast body of robotics research. Last October, scientists at the company published a paper detailing a machine learning system dubbed Form2Fit, which aims to teach a picker robot with a suction arm the concept of assembling objects into kits. Google Brain researchers are pursuing a novel robot task planning technique involving deep dynamics models, or DDM, that they claim enables mechanical hands to manipulate multiple objects. And more recently, a Google team took the wraps off of ClearGrasp, an AI model that helps robots better recognize transparent objects.

As the researchers point out, until recently, most automated solutions were designed for rigid settings where scripted robot actions are repeated to move through a predefined set of positions. This approach calls for a highly calibrated setup that can be expensive and time-consuming, and one that lacks the robustness needed to handle changes in the environment. Advancements in computer vision have led to better performance in grasping, but tasks like stacking, insertion, and precision kitting remain challenging. That’s because they require accurate 3D geometric knowledge of the task environment including object shape and pose, relative distances and orientation between locations, and other factors.

By contrast, the team’s method leverages a multi-camera view and a reinforcement learning framework that takes in images from different viewpoints and produces robot actions in a closed-loop fashion. By combining and learning directly from the camera views without an intermediary reconstruction step, they say it’s able to improve state estimation while at the same time increasing the robustness of the system’s actions.

In experiments, the researchers deployed their setup to a simulated environment containing a Kuka arm equipped with a gripper, two bins placed in front of the robot, and three cameras mounted to overlook those bins. The arm was first tasked with stacking one bin with a single block in a random position, starting with a single block either blue or orange in color. In other tasks, it had to insert a block firmly into a middle fixture and to stack blocks one on top of the other.

The researchers ran 180 data collection jobs across 10 graphics cards to train their reinforcement learning model, with each producing roughly 5,000 episodes per hour for the insertion tasks. They report it achieved success, with “large reductions” to error rates on precision-based tasks — specifically 49.18% on the first stacking task, 56.84% on the second stacking task, and 64.1% on the insertion task. “The effective use of multiple views enables a richer observation of the underlying state relevant to the task,” wrote the paper’s coauthors. “Our multi-view approach enables 3D tasks from RGB cameras without the need for explicit 3D representations and without camera-camera and camera-robot calibration. In the future, similar multi-view benefits can be achieved with a single mobile camera by learning a camera placement policy in addition to the task policy.”

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Researchers propose data set to measure few-shot learning performance

February 20, 2020 Big Data

A plethora of AI models have been tailored to tackle few-shot classification, which refers to learning a classifier for new classes given only a few examples (an ability humans naturally possess). Improving on it could lead to more efficient algorithms capable of expanding their knowledge without requiring large labeled data sets, but to date, many of the procedures and corpora used to assess progress here are lacking.

That’s why researchers at Google AI, the University of California, Berkeley, and the University of Toronto propose in a preprint paper a benchmark for training and evaluating large-scale, diverse, and more “realistic” few-shot classification models. They say it improves upon previous approaches by incorporating multiple data sets of “diverse” distributions and by introducing realistic class imbalance, which they say allows the testing of robustness across a spectrum from low-shot learning onward.

The work was published in May 2019, but it was recently accepted to the International Conference on Learning Representations (ICLR) that’ll be held in Addis Ababa, Ethiopia in April.

As the team explains, as opposed to synthetic environments, real-life learning experiences are heterogeneous in that they vary by the number of classes and the examples per class. They also measure only within-corpus generalization, and they ignore the relationships between classes when forming episodes — i.e., the coarse-grained classification of dogs and chairs may present different difficulties than the fine-grained classification of dog breeds. (An “episode” encompasses states that come between an initial-state and a terminal-state, such as a game of Chess.)

By contrast, the researchers’ data set — the Meta-Dataset — leverages data from 10 different corpora, which span a variety of visual concepts natural and human-made and vary in the specificity of the class definition. Two are reserved for evaluation, meaning that no two classes from them participate in the training set, while the remaining ones contribute some classes to each of the training, validation, and test splits of classes.

Meta-Dataset separately employs an algorithm for sampling episodes, which aims to yield imbalanced episodes of variable shots (class precision) and ways (accuracy). A prescribed number of examples of each chosen class are chosen uniformly at random to populate the support and query sets.

In experiments, the team trained meta-learning models via training episodes sampled using the same algorithm as used for Meta-Dataset’s evaluation episodes. They say that, tested against Meta-Dataset, the models generally didn’t improve when provided multiple data sources and that they didn’t benefit from meta-learning across the data sets. Moreover, they report that the models weren’t robust to the amount of data in test episodes; rather, each excelled in a different part of the spectrum.

“We believe that our exploration of various models on Meta-Dataset has uncovered interesting directions for future work pertaining to meta-learning across heterogeneous data,” wrote the coauthors, who added that addressing the uncovered shortcomings constitutes an important research goal. “[I]t remains unclear what is the best strategy for creating training episodes, the most appropriate validation creation and the most appropriate initialization.”

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Google’s ML-fairness-gym lets researchers study the long-term effects of AI’s decisions

February 6, 2020 Big Data

Determining whether an AI system is maintaining fairness in its predictions requires an understanding of models’ short- and long-term effects, which might be informed by disparities in error metrics on a number of static data sets. In some cases, it’s necessary to consider the context in which the AI system operates in addition to the aforementioned error metrics, which is why Google researchers developed ML-fairness-gym, a set of components for evaluating algorithmic fairness in simulated social environments.

ML-fairness-gym — which was published in open source on Github this week –is designed to be used to research the long-term effects of automated systems by simulating decision-making using OpenAI’s Gym framework. AI-controlled agents interact with digital environments in a loop, and at each step, an agent chooses an action that affects the environment’s state. The environment then reveals an observation that the agent uses to inform its next actions, so that the environment models the system and dynamics of a problem and the observations serve as data.

For instance, given the classic lending problem, where the probability that groups of applicants pay back a bank loan is a function of their credit score, the bank acts as the agent and receives applicants, their scores, and their membership in the form of environmental observations. It makes a decision — accepting or rejecting a loan — and the environment models whether the applicant successfully repays or defaults and adjusts their credit score accordingly. Throughout, ML-fairness-gym simulates the outcomes so that the effects of the bank’s policies on fairness to the applicants can be assessed.

ML-fairness-gym in this way cleverly avoids the pitfalls of static data set analysis. If the test sets (i.e., corpora used to evaluate model performance) in classical fairness evaluations are generated from existing systems, they may be incomplete or reflect the biases inherent to those systems. Furthermore, the actions informed by the output of AI systems can have effects that might influence their future input.

Google’s ML fairness gym lets researchers study the long term effects of AI’s decisions

Above: In the lending problem scenario, this graph illustrates changing credit score distributions for two groups over 100 steps of simulation.

Image Credit: Google

“We created the ML-fairness-gym framework to help ML practitioners bring simulation-based analysis to their ML systems, an approach that has proven effective in many fields for analyzing dynamic systems where closed form analysis is difficult,” wrote Google Research software engineer Hansa Srinivasan in a blog post.

Several environments that simulate the repercussions of different automated decisions are available, including one for college admissions, lending, attention allocation, and infectious disease. (The ML-fairness-gym team cautions that the environments aren’t meant to be hyper-realistic and that best-performing policies won’t necessarily translate to the real world.) Each have a set of experiments corresponding to published papers, which are meant to provide examples of ways ML-fairness-gym can be used to investigate outcomes.

The researchers recommend using ML-fairness-gym to explore phenomena like censoring in the observation mechanism, errors from the learning algorithm, and interactions between the decision policy and the environment. The simulations allow for the auditing of agents to assess the fairness of decision policies based on observed data, which can motivate data collection policies. And they can be used in concert with reinforcement learning algorithms — algorithms that spur on agents with rewards — to derive new policies with potentially novel fairness properties.

In recent months, a number of corporations, government agencies, and independent researchers have made attempts at tackling the so-called “black box” problem in AI — the opaqueness of some AI systems — with varying degrees of success.

“Machine learning systems have been increasingly deployed to aid in high-impact decision-making, such as determining criminal sentencing, child welfare assessments, who receives medical attention and many other settings,” continued Srinivasan. “We’re excited about the potential of the ML-fairness-gym to help other researchers and machine learning developers better understand the effects that machine learning algorithms have on our society, and to inform the development of more responsible and fair machine learning systems.”

In 2017, the U.S. Defense Advanced Research Projects Agency launched DARPA XAI, a program that aims to produce “glass box” models that can be easily understood without sacrificing performance. In August, scientists from IBM proposed a “factsheet” for AI that would provide information about a model’s vulnerabilities, bias, susceptibility to adversarial attacks, and other characteristics. A recent Boston University study proposed a framework to improve AI fairness. And Microsoft, IBM, Accenture, and Facebook have developed automated tools to detect and mitigate bias in AI algorithms.

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SenseTime researchers create a benchmark to test face forgery detectors

January 16, 2020 Big Data

Face swapping is a category of deepfakes that extracts the faces of people in existing media and replaces them with other peoples’ features, typically with AI and machine learning. It’s been popularized by apps like MixBooth and SnapChat, and while the underlying techniques have enabled sophisticated image editing for legitimate purposes, they’ve also given rise to concerns about potential misuse or abuse.

Various groups have compiled manipulated media to support the development of face swapping detection methods, but the samples that have released so far are relatively few in number or overly artificial. That’s why researchers from SenseTime Research, the R&D division of Hong Kong-based tech startup SenseTime, partnered with Nanyang Technological University in Singapore to design a new large-scale benchmark for face forgery detection. They call it DeeperForensics-1.0, and they say it’s the largest corpora of its kind with over 60,000 videos containing roughly 17.6 million frames.

According to the researchers, all source videos in DeeperForensics-1.0 were carefully selected for their quality and diversity. They’re ostensibly more realistic than those in other data sets in that they’re closer in kind to real-world detection scenarios, and in that they contain compression, blurriness, and transmission artifacts matching those found in the wild.

To build DeeperForensics-1.0, the researchers collected face data from 100 paid male and female actors of 26 different nationalities and ranging in age from 20 to 45, all of whom were instructed to turn their heads in nine lighting conditions and speak naturally with over 53 expressions. They ran these through an AI framework — DeepFake Variational AutoEncoder, or DF-VAE — using 1,000 YouTube videos as target videos, where each of the 100 actors’ faces was swapped onto 10 targets. And they deliberately distorted each video in 35 different ways to simulate real-world scenarios, such that the final data set contained 50,000 unmanipulated videos and 10,000 manipulated videos.

“We find that the source faces play a much more critical role than the target faces in building a high-quality data set,” wrote the researchers in a preprint paper detailing their work. “Specifically, the expressions, poses, and lighting conditions of source faces should be much richer in order to perform robust face swapping.”

The researchers also created what they call a “hidden” test set within DeeperForensics-1.0 — a set of 400 videos carefully selected to better imitate fake videos in real scenes. Curating the set involved collecting fake videos generated by unknown face-swapping methods and obscuring them with distortions commonly seen in real scenes, and subsequently choosing only videos that fooled at least 50 out of 100 human observers in a user study.

To evaluate the quality of DeeperForensics-1.0 compared with other publicly available data sets, the researchers tasked 100 experts in computer vision with ranking the the quality of a subset of videos contained within it. They report that DeeperForensics-1.0 came out ahead on average in terms of realism for its scale compared with FaceForensics++, Celeb-DF, and other popular deepfake detection corpora.

In future work, the research team intends to expand DeeperForensics gradually and work with the research community toward identifying evaluation metrics for face forgery detection methods.

The fight against deepfakes appears to be ramping up. Last summer, members of DARPA’s Media Forensics program tested a prototypical system that could automatically detect AI-generated videos in part by looking for cues like unnatural blinking. Startups like Truepic, which raised an $ 8 million funding round in July, are experimenting with deepfakes “detection-as-a-service” business models. And in December 2019, Facebook together with the Partnership on AI, Microsoft, and academics launched the Deepfake Detection Challenge, which will offer millions of dollars in grants and awards to spur the development of deepfake-detecting systems.

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Carnegie Mellon researchers propose AI that surfaces positive online comments

January 13, 2020 Big Data

Researchers at Carnegie Mellon’s Language Technologies Institute say they’ve developed a system that taps machine learning to analyze online comments and pick out those that defend or sympathize with disenfranchised peoples. Although it hasn’t been commercialized, they’ve used it in experiments to search for nearly a million YouTube comments, focusing on the Rohingya refugee crisis and the February 2019 Pulwama terrorist attack in Kashmir. And they hope it’ll form the foundation of a future system that’ll reduce the manual effort necessary to curate comments on publisher websites, social media, and elsewhere.

Tamping down on abusive online behavior is no easy feat, particularly considering the level of toxicity in some social circles. More than one in five respondents to a survey by the Anti-Defamation League, a nonprofit that tracks and fights anti-Semitism, reported having been subjected to threats of violence. And nearly one in five said they’d experienced sexual harassment or stalking and sustained harassment, while upwards of 20% said the harassment was the result of their gender identity, race, ethnicity, sexual orientation, religion, occupation, or disability.

As the researchers explain, improvements in AI language models — which learn from many examples to predict what words are likely to occur in a given sentence — made it possible to analyze such large quantities of text. The study’s contribution was a technique enabling those models to digest short texts originating from South Asia, which can be difficult to interpret because they often contain spelling and grammar mistakes and combine different languages and systems of writing.

Specifically, the researchers obtained embeddings — numerical representations of words — that revealed novel language groupings or clusters. Language models create these so that words with similar meanings are represented in the same way, making it possible to compute the proximity of a word to others in a comment or post.

The team reports that in experiments, their approach worked as well or better than commercially available solutions. Random samplings of the YouTube comments showed about 10% were positive, compared with the 88% found with the AI algorithm.

“Even if there’s lots of hateful content, we can still find positive comments,” said post-doctoral research Ashiqur R. KhudaBukhsh, a contributing author on aforthcoming paper describing the work. He’ll present his findings with coauthors Shriphani Palakodety and Jaime Carbonell at the Association for the Advancement of Artificial Intelligence annual conference next month in New York City.

The study follows the release of a data set by Jigsaw — the organization working under Google parent company Alphabet to tackle cyber bullying, censorship, disinformation, and other digital issues of the day — containing hundreds of thousands of comments and annotations with toxicity and identity labels. It’s intended to help measure bias in AI comment classification systems, which Jigsaw and others have historically measured using synthetic data from template sentences.

In a related development, researchers at the Georgia Institute of Technology and email marketing startup Mailchimp recently proposed RECAST, an interactive tool for examining toxicity detection models by visualizing explanations for predictions and providing alternative wordings for detected toxic speech. The plan to release an open source browser extension in the near future.

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Intel researchers propose AI that recognizes faces from thermal images

January 10, 2020 Big Data

Is thermal imagery detailed enough to enable an AI model to recognize peoples’ facial features? That’s the question Intel and Gdansk University of Technology researchers sought to answer in study recently presented at the Institute of Electrical and Electronics Engineers’ 12th International Conference on Human System Interaction. In it, they investigated the performance of a model trained on visible light data that was subsequently retrained on thermal images.

As the researchers point in a paper describing their work, thermal imagery is often used in lieu of RGB camera data within environments where privacy is preferred or otherwise mandated, like medical facilities. That’s because it’s able to obscure personally identifying details like eye color and jaw line.

The team tapped two data sets of facial thermal images., the first of which — SC3000-DB — was created using a Flir ThermaCam SC3000 infrared camera. It contained 766 images of 40 volunteers from a cohort of 19 men and 21 women, who were asked to sit and look at the camera for a span of two minutes. As for the second data set — the IRIS data set, from the Visual Computing and Image Processing Lab at Oklahoma State University — it contained 4,190 images collected with the assistance of 30 people, many of whom moved their heads and used different facial expressions.

The researchers first cropped the images to regions containing only peoples’ faces, using a machine learning model. Then, they employed another model to extract facial features from the images and represent them numerically, as vectors (sets of values corresponding to specific features). Lastly, they employed a third model trained on visible light images to validate whether such a model could be applied to thermal images.

In the course of experiments, the researchers compared two types of facial feature vectors: one that identified the person depicted in a given image and a second based on the similarity of the person’s profile with an input image. They found that the model trained on visible image data generalized well to thermal images, successfully extracting facial features and distinguishing among volunteers with accuracies of 99.5% for SC3000-DB and 82.14% for IRIS.

The team hopes to extend their research to data collected in various other scenarios, such as when subjects’ heads are turned horizontally or vertically. “Many promising visual-processing applications, such as non-contact vital signs estimation and smart home monitoring, can involve private and or sensitive data, such as biometric information about a person’s health,” wrote the researchers. “Thermal imaging, which can provide useful data while also concealing individual identities, is therefore used for many applications.”

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Researchers detail AI that de-hazes and colorizes underwater photos

December 31, 2019 Big Data

Ever notice that underwater images tend to be be blurry and somewhat distorted? That’s because phenomena like light attenuation and back-scattering adversely affect visibility. To remedy this, researchers at Harbin Engineering University in China devised a machine learning algorithm that generates realistic water images, along with a second algorithm that trains on those images to both restore natural color and reduce haze. They say that their approach qualitatively and quantitatively matches the state of the art, and that it’s able to process upwards of 125 frames per second running on a single graphics card.

The team notes that most underwater image enhancement algorithms (such as those that adjust white balance) aren’t based on physical imaging models, making them poorly suited to the task. By contrast, this approach taps a generative adversarial network (GAN) — an AI model consisting of a generator that attempts to fool a discriminator into classifying synthetic samples as real-world samples — to produce a set of images of specific survey sites that are fed into a second algorithm, called U-Net.

The team trained the GAN on a corpus of labeled scenes containing 3,733 images and corresponding depth maps, chiefly of scallops, sea cucumbers, sea urchins, and other such organisms living within indoor marine farms. They also sourced open data sets including NY Depth, which comprises thousands of underwater photographs in total.

Post-training, the researchers compared the results of their twin-model approach to that of baselines. They point out that their technique has advantages in that it’s uniform in its color restoration, and that it recovers green-toned images well without destroying the underlying structure of the original input image. It also generally manages to recover color while maintaining “proper” brightness and contrast, a task at which competing solutions aren’t particularly adept.

It’s worth noting that the researchers’ method isn’t the first to reconstruct frames from damaged footage. Cambridge Consultants’ DeepRay leverages a GAN trained on a data set of 100,000 still images to remove distortion introduced by an opaque pane of glass, and the open source DeOldify project employs a family of AI models including GANs to colorize and restore old images and film footage. Elsewhere, scientists at Microsoft Research Asia in September detailed an end-to-end system for autonomous video colorization; researchers at Nvidia last year described a framework that infers colors from just one colorized and annotated video frame; and Google AI in June introduced an algorithm that colorizes grayscale videos without manual human supervision.

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Researchers want to use Mega Man 2 to evaluate AI

December 26, 2019 Big Data

Games have long served as a training ground for AI algorithms, and not without good reason. Games — particularly video games — provide challenging environments against which to benchmark autonomous systems. In 2013, a team of researchers introduced the Arcade Learning Environment, a collection of over 55 Atari 2600 games designed to test a broad range of AI techniques. More recently, San Francisco research firm OpenAI detailed Procgen Benchmark, a set of 16 virtual worlds that measure how quickly models learn generalizable skills.

The next frontier might be Mega Man, if an international team of researchers have their way. In a newly published paper on the preprint server Arxiv.org, they propose EvoMan, a game-playing competition based on the eight boss fights in Capcom’s cult classic Mega Man 2. As they describe it, competitors’ goal is to train an AI agent to defeat every enemy and evaluate their performances by common metrics.

Why Mega Man 2? The paper’s coauthors assert that few other environments test an AI’s ability to win against a single enemy, or how well an AI generalizes to win matches against waves of enemies or coevolves to create increasingly difficult enemies. To this end, in EvoMan, an AI-controlled Mega Man — a robot equipped with a powerful arm cannon — must beat eight so-called Robot Masters equipped with different weapons. Every time a Robot Master is defeated, the agent acquires its weapon, making it easier to defeat the bosses who remain.

Researchers want to use Mega Man 2 to evaluate AI

Above: A table of scores achieved by an agent in the EvoMan benchmark.

Image Credit: EvoMan

As proposed, EvoMan challenge entrants would train their agents on any four enemies and measure how well their learned strategy scales up to a whole set of enemies. The agents would be expected to learn how to identify and react to general patterns like avoiding being shot or shooting at the direction of the enemy, and to deplete an enemy’s health from 100 energy points to 0 by the end of each match.

“The main goal of this competition is that a given agent perform equally good for every boss,” wrote the coauthors, who suggest that competitors be ranked by the mean of performances across bosses. “The winner … will be the one agent that performs equally well on each one of the eight bosses, hopefully defeating them all.”

Atari titles and Mega Man 2 are far from the only games against which AI has been trained and evaluated. In July, a paper published by Facebook researchers posited that Minecraft was well-suited to refining natural language understanding algorithms, and both OpenAI and Google parent company Alphabet’s DeepMind have fine-tuned systems on Valve’s Dota 2 and Activision Blizzard’s Starcraft 2. Separately, scientists at Facebook AI Research, the Lorraine Research Laboratory in Computer Science and its Applications, and the University College London are developing LIGHT, an open source research environment in the form of a large-scale, crowdsourced text adventure within which AI and humans interact as player characters.

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Tag Archives: researchers

IBM makes AI tools and resources available to coronavirus researchers

Researchers propose paradigm that trains AI agents through evolution

Google researchers use multiple cameras to reduce error rates for robot insertion and stacking tasks

Researchers propose data set to measure few-shot learning performance

Google’s ML-fairness-gym lets researchers study the long-term effects of AI’s decisions

SenseTime researchers create a benchmark to test face forgery detectors

Carnegie Mellon researchers propose AI that surfaces positive online comments

Intel researchers propose AI that recognizes faces from thermal images

Researchers detail AI that de-hazes and colorizes underwater photos

Researchers want to use Mega Man 2 to evaluate AI

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